System and method for segmenting medical image

ABSTRACT

A method for segmenting a medical image is disclosed. The method includes acquiring MR image and PET data during a scan of the object, acquiring an air/bone ambiguous region in the MR image, the air/bone ambiguous region including air voxels and bone voxels undistinguished from each other. The method also includes assigning attenuation coefficients to the voxels of the plurality of regions and generating an attenuation map. The method further includes iteratively reconstructing the PET data and the attenuation map to generate a PET image and an estimated attenuation map. The method further includes reassigning attenuation coefficients to the voxels of the air/bone ambiguous region based on the estimated attenuation map, and distinguishing the bone voxels and air voxels in the air/bone ambiguous region.

CROSS-REFERENCE TO RELATED APPLICATION

The application is a Continuation of U.S. application Ser. No.15/215,575, filed on Jul. 20, 2016, now U.S. Pat. No. 10,210,634, thecontents of which are hereby incorporated by reference.

TECHNICAL FIELD

This application generally relates to a system and method for segmentingmedical images, and specifically, relates to a system and method forsegmenting bone and air in an MR image based on nuclear emission data.

BACKGROUND

Radionuclide imaging, including single photon emission computedtomography (SPECT) and Positron emission tomography (PET), etc., is aquantitative technology for imaging metabolic pathways and dynamicprocesses in vivo. It may reflect the change of cell metabolism andfunction at the molecular level. In a clinical environment, radionuclideimages may be interpreted visually to assess the physiologic function oftissues, organs, and/or organ systems. Magnetic resonance (MR) imagingmay provide versatile soft-tissue contrast, yielding diagnostic accuracywithout exposing a patient to ionizing radiation. The combination of PETimaging and MR imaging may provide many advantages such as highersoft-tissue contrast, reduced radiation exposure, and advanced MRimaging techniques such as diffusion imaging, perfusion imaging, and MRspectroscopy. However, the combination is challenging because of lowsignal intensity of cortical bone in conventional MR imaging. Low signalintensity of cortical bone makes it difficult to differentiate bonetissue from air cavities in MR imaging.

Thus, there exists a need in the field to provide a method and systemfor the separation of bone and air in MR image.

SUMMARY

The present disclosure provided herein relates to segmenting medicalimages. Specifically, the present disclosure relates to a system andmethod for segmenting bone and air in MR image using nuclear emissiondata.

Improved PET data quantification is expected to be a main advantage ofPET/MR, and an accurate attenuation-correction method is necessary forobtaining a precise quantitative measure of the radiotracerconcentration. However, the MR imaging signal reflects tissue protondensities or tissue relaxation times, and not electron density. The MRimages are not directly related to the tissue linear attenuationcoefficients. They can lack quantification accuracy in brain imaging andin certain whole-body application, such as the imaging of lung and bonemarrow, when the segmented tissue classes have high attenuationcoefficient variability or too few tissue classes are used.

A method for segmenting medical image may be performed on an anatomicimage so as to facilitate the tissue classification or identification.The method may include one or more of the following operations. A firsttype of medical image may be acquired via reconstructing a first type ofdata; the first type of medical image may include a plurality of voxels.A second type of data for reconstructing a second type of medical imagemay be acquired. The first type of medical image may be executedpreliminary segmentation, and the first type of medical image may besegmented into a plurality of regions. The plurality of regions mayinclude unambiguous regions and ambiguous regions. An attenuation mapmay be generated by assigning different attenuation coefficientscorresponding to voxels belonging to different regions of the first typeof image being executed preliminary segmentation. The second type ofmedical image may be acquired via iteratively reconstructing the secondtype of data based on the attenuation map. The attenuation map may beupdated after each iteration. An estimated attenuation map may beacquired via iteratively reconstructing the attenuation map during theiterative reconstruction of the second type of data. The voxels of thefirst type of medical image may be reassigned attenuation coefficientsbased on the estimated attenuation map. The first type of medical imagemay be segmented into a plurality of sub-regions distinguished from eachother.

In some embodiments, the first type of medical image may be an MR image,and the MR image may be segmented based on at least one approachselected from a thresholding approach, a region growing approach, ahistogram approach, an atlas-guided approach, or a clustering approach.

In some embodiments, the ambiguous region may include at least anair/bone ambiguous region, and the unambiguous region includes at leasta soft-tissue region.

In some embodiments, values of the voxels in the soft-tissue region maybe similar to each other, and values of the voxels in the air/boneambiguous region may be similar to each other.

In some embodiments, the voxels of the air/bone ambiguous region may beassigned specific attenuation coefficients.

In some embodiments, the specific attenuation coefficients may be equalto attenuation coefficient of water.

In some embodiments, the first type of medical image may be produced byMR device and the second type of medical image is produced by PETdevice.

In some embodiments, the unambiguous region may include a firstsoft-tissue region and a second soft-tissue region, and the voxels ofthe first soft-tissue region may be assigned a first attenuationcoefficient, and the voxels of the second soft-tissue region may beassigned a second attenuation coefficient.

In some embodiments, the first attenuation coefficient or the secondattenuation coefficient may be updated after each iteration.

In some embodiments, the attenuation coefficient of each voxel in theair/bone ambiguous region may be updated after each iteration.

In another aspect of the present disclosure, a method for segmenting amedical image may include one or more of the following operations. MRdata may be acquired during a scan of an object using an MR scanner. AnMR image may be reconstructed using the MR data, and the MR imageincludes a plurality of voxels. PET data may be acquired during a scanof the object using a PET scanner. The MR image may be segmented into aplurality of regions which include at least an air/bone ambiguousregion. The air/bone ambiguous region may include air voxels and bonevoxel undistinguished from each other. An attenuation map may begenerated by assigning attenuation coefficients to the voxels of theplurality of regions. The voxels of the air/bone ambiguous region may beassigned specific attenuation coefficients. The PET data and theattenuation map may be iteratively reconstructed. A PET image and anestimated attenuation map may be generated. The voxels of the air/boneambiguous region may be reassigned attenuation coefficients based on theestimated attenuation map.

In some embodiments, the plurality of regions of the MR image mayinclude a soft-tissue region; the soft-tissue region may include voxelsof a soft-tissue. The air/bone ambiguous region may be acquired byexcluding the voxels of the soft-tissue regions from the MR image.

In some embodiments, values of the voxels in the soft-tissue region maybe similar to each other in the MR image, and values of the voxels inthe air/bone ambiguous region may be similar to each other in the MRimage.

In some embodiments, the attenuation coefficient of each voxel in theair/bone ambiguous region may be updated after each iteration.

In some embodiments, the specific attenuation coefficients of the voxelsin the air/bone ambiguous region may be equal to the attenuationcoefficients of water.

In some embodiments, the air/bone ambiguous region may be segmented intoa first region and a second region. The first region may include onlythe bone voxels and the second region includes only the air voxels.

In another aspect of the present disclosure, a system for segmenting amedical image is provided. The system may include an image processor,and the image processor may include a first reconstruction module, asegmentation module and a second reconstruction module. The firstreconstruction module may acquire an MR image via reconstructing the MRdata. The segmentation module may segment the MR image into at least anair/bone ambiguous region and a soft-tissue region, the air/boneambiguous region may include air voxels and bone voxels undistinguishedfrom each other, and the soft-tissue region may include only voxels ofsoft-tissue. The second reconstruction module may assign initialattenuation coefficient to each voxel of the MR image being segmented,and generate an attenuation map. Voxels of the air/bone ambiguous regionmay be assigned attenuation coefficient of water. The secondreconstruction module may iteratively update the PET data and theattenuation map to generate a PET image and an estimated attenuationmap. The second reconstruction module may further reassign attenuationcoefficient to each voxel of the MR image being segmented based on theestimated attenuation map.

In some embodiments, the image processor may further segment theair/bone ambiguous region into a first region, a second region, and athird region. The first region may include only voxels of a rib, thesecond region may include only voxels of a spine, and the third regionmay include only voxels of a lung.

In some embodiments, the attenuation coefficient corresponding to thebone voxels in the first region may be different from the attenuationcoefficient corresponding to the air voxels in the second region.

In some embodiments, the system for segmenting a medical image mayfurther include an MR scanner, a PET scanner, and a display. The MRscanner may acquire MR data during a first scan of an object. The PETscanner may acquire PET data during a second scan of the object. Thedisplay may simultaneously display the PET image and the MR image in anoverlaying manner.

Additional features will be set forth in part in the description whichfollows, and in part will become apparent to those skilled in the artupon examination of the following and the accompanying drawings or maybe learned by production or operation of the examples. The features ofthe present disclosure may be realized and attained by practice or useof various aspects of the methodologies, instrumentalities andcombinations set forth in the detailed examples discussed below.

BRIEF DESCRIPTION OF THE DRAWINGS

The present disclosure is further described in terms of exemplaryembodiments. These exemplary embodiments are described in detail withreference to the drawings. These embodiments are non-limiting examples,in which like reference numerals represent similar structures throughoutthe several views of the drawings, and wherein:

FIG. 1 illustrates a hybrid PET/MR imaging system according to someembodiments of the present disclosure;

FIG. 2 illustrates a block diagram of an MR scanner according to someembodiments of the present disclosure;

FIG. 3 illustrates a block diagram of a PET scanner according to someembodiments of the present disclosure;

FIG. 4 illustrates a block diagram of a processor according the someembodiments of the present disclosure;

FIG. 5 illustrates a flowchart for segmenting a medical image accordingto some embodiments of the present disclosure;

FIG. 6 illustrates another flowchart for segmenting a medical imageaccording to some embodiments of the present disclosure;

FIG. 7 illustrates a flowchart for segmenting an MR image based on PETdata according to some embodiments of the present disclosure;

FIG. 8 illustrates another flowchart for segmenting an MR image based onPET data according to some embodiments of the present disclosure;

FIG. 9 illustrates a preliminary segmentation of an MR image accordingto some embodiments of the present disclosure; and

FIG. 10 illustrates an estimated attenuation map according to someembodiments of the present disclosure.

DETAILED DESCRIPTION

In the following detailed description, numerous specific details are setforth by way of example in order to provide a thorough understanding ofthe relevant disclosure. However, it should be apparent to those skilledin the art that the present disclosure may be practiced without suchdetails. In other instances, well known methods, procedures, systems,components, and/or circuitry have been described at a relativelyhigh-level, without detail, in order to avoid unnecessarily obscuringaspects of the present disclosure. Various modifications to thedisclosed embodiments will be readily apparent to those skilled in theart, and the general principles defined herein may be applied to otherembodiments and applications without departing from the spirit and scopeof the present disclosure. Thus, the present disclosure is not limitedto the embodiments shown, but to be accorded the widest scope consistentwith the claims.

It will be understood that the term “system,” “engine,” “unit,”“module,” and/or “block” used herein are one method to distinguishdifferent components, elements, parts, section or assembly of differentlevel in ascending order. However, the terms may be displaced by otherexpression if they may achieve the same purpose.

It will be understood that when a unit, engine, module or block isreferred to as being “on,” “connected to,” or “coupled to” another unit,engine, module, or block, it may be directly on, connected or coupledto, or communicate with the other unit, engine, module, or block, or anintervening unit, engine, module, or block may be present, unless thecontext clearly indicates otherwise. As used herein, the term “and/or”includes any and all combinations of one or more of the associatedlisted items.

The terminology used herein is for the purposes of describing particularexamples and embodiments only, and is not intended to be limiting. Asused herein, the singular forms “a,” “an,” and “the” may be intended toinclude the plural forms as well, unless the context clearly indicatesotherwise. It will be further understood that the terms “include,”and/or “comprise,” when used in this disclosure, specify the presence ofintegers, devices, behaviors, stated features, steps, elements,operations, and/or components, but do not exclude the presence oraddition of one or more other integers, devices, behaviors, features,steps, elements, operations, components, and/or groups thereof. It willbe further understood that the terms “constructed” and “reconstruct”,when used in this disclosure, may represent a similar process that animage may be transformed from data. In some embodiments, the terms“reconstruct,” “estimate” and “update,” when used in this disclosure,may represent a similar process of data or image correction.

In some embodiments, the object or subject may be a human being, ananimal, an organ, a texture, a region, a lesion, a tumor, or the like,or any combination thereof. Merely by way for example, the object mayinclude a head, a breast, a lung, a trachea, a pleura, a mediastinum, anabdomen, a long intestine, a small intestine, a bladder, a gallbladder,a triple warmer, a pelvic cavity, a backbone, extremities, a skeleton, ablood vessel, or the like, or any combination thereof. In someembodiments, the medical image may include a 2D image and/or a 3D image.In some embodiments, the 3D image may include a series of 2D slices or2D layers.

For illustration purposes, the following description is provided to helpbetter understanding an image processing. It is understood that this isnot intended to limit the scope of the present disclosure. For personshaving ordinary skills in the art, a certain amount of variations,changes and/or modifications may be deducted under guidance of thepresent disclosure. However, those variations, changes and/ormodifications do not depart from the scope of the present disclosure.

The application relates to a system and method for segmenting medicalimages, and specifically, relates to a system and method for segmentingbone and air in an MR image based on nuclear emission data. The systemand method disclosed herein exploit the difference between theattenuation properties of bone and air in functional images including,for example, PET images, SPECT images, etc. For instance, bone andcavities that may show similar signal intensities in MR imaging maydisplay different tissue attenuations in PET. The system and methoddisclosed herein may facilitate resolving ambiguity between, forexample, bone and air, in an MR image based on a functional image.

FIG. 1 shows an exemplary diagram of an imaging system 100 forsegmenting an image according to some embodiments of the presentdisclosure. In some embodiments, the imaging system 100 may be a hybridimaging system. In some embodiments, the hybrid imaging system may be aPET/MR imaging system, or a PET/CT imaging system. In some embodiments,the hybrid imaging system may acquire data of various types. Forexample, a first type of data (also referred to as “first data”) (e.g.,MR data, CT data, or the like) and a second type of data (also referredto as “second data”) (e.g., PET data, or the like) may be acquired. Afirst type of image (also referred to as “first image”) (e.g., an MRimage, a CT image, or the like) and a second type of image (alsoreferred to as “second image”) (e.g., a PET image, or the like) may begenerated based on the first type of data and the second type of data,respectively. For illustration purposes, as described in FIG. 1, theimaging system 100 may include a hybrid PET/MR imaging system. Thehybrid PET/MR imaging system 100 may include an MR scanner 110, a PETscanner 120, an image processor 130, a controller 140 and a display 150.

The MR scanner 110 may scan an object (e.g., a portion of a subject) toacquire a plurality of MR data relating to the object. The PET scanner120 may scan the object to acquire a plurality of PET data relating tothe object. In some embodiments, the MR scanner 110 and the PET scannermay be integrated in the hybrid system. In some embodiments, the MR dataand the PET data may be acquired simultaneously or successively. In someembodiments, the acquired MR data and PET data may be stored in astorage (e.g., a disk, a cloud storage, not shown in FIG. 1), and may beretrieved when needed.

The image processor 130 may process the MR data and the PET data. Insome embodiments, the image processor 130 may generate one or moreimages or maps based on the MR data and the PET data. The images or mapsmay include an MR image, a PET image, an attenuation map, an updated PETimage, an estimated attenuation map, or the like, or a combinationthereof. The MR image may be generated based on the MR data. The PETimage may be generated based on the PET data. In some embodiments, thePET image and the attenuation map may be iteratively updated (alsoreferred to as an “iterative reconstruction process”) simultaneously orsuccessively, and an estimated (also referred to as “updated”) PET imageand an estimated (also referred to as “updated”) attenuation map may begenerated. As used herein, the attenuation map may be generated byassigning attenuation coefficients to the voxels of the MR image; theestimated attenuation map may be acquired by iteratively updating theattenuation map during the iterative reconstruction process.

In some embodiments, the MR image may be segmented into a plurality ofregions, including, e.g., at least an ambiguous region and at least anunambiguous region. As used herein, an ambiguous region may refer tothat values of voxels in the region may be similar to each other, andtherefore the voxels in the region may be undistinguishable from eachother. In some embodiments, the ambiguous region may be an air/boneambiguous region, and the unambiguous region may be a soft-tissueregion. In some embodiments, in the air/bone ambiguous region, airvoxels and bone voxels may be undistinguishable from each other.

In some embodiments, the image processor 130 may register the MR imagewith respect to the PET image, or register the PET image with respect tothe MR image. The registration may be performed based on an optical flowmethod, a registration method based on one or more feature points, aregistration method based on a contour, a registration method based ongrey scale information, etc.

In some embodiments, the image processor 130 may be local or remote. Insome embodiments, the MR scanner 110 and the PET scanner 120 may sharethe image processor 130, or employ their respective image processors.

The controller 140 may control the acquisition process, the processingprocedure, or a display process. For example, the controller 140 maycontrol the scanning procedure in the MR scanner 110 or the PET scanner120. As another example, the controller 140 may control a displayparameter (e.g., display size, display scale, resolution, display color,or the like, or a combination thereof) of the display 150. In someembodiments, the controller 140 may be a central processing unit (CPU),an application-specific integrated circuit (ASIC), anapplication-specific instruction-set processor (ASIP), a graphicsprocessing unit (GPU), a physics processing unit (PPU), amicrocontroller unit, a digital signal processor (DSP), a fieldprogrammable gate array (FPGA), an ARM, or the like, or any combinationthereof.

The display 150 may simultaneously or successively display the PET imageand the MR image. In some embodiments, the PET image and the MR imagemay be displayed in an overlaying manner. The display may include acathode ray tube (CRT) display, a liquid crystal display (LCD), anorganic light emitting display (OLED), a plasma display, or the like, orany combination thereof. In some embodiments, the display 150 may beintegrated into a display device. The display device may include acomputer, a laptop, a cell phone, a mobile phone, a pad, a glass, aprojector, a virtual reality device, or the like, or any combinationthereof. In some embodiments, the display 150 may display characteristicinformation of the object, including height, weight, gender, age, amedical condition of the object, a medical history of the object, anarea of interest of the object, or the like, or any combination thereof.The information of the area of interest of the object may furtherinclude the position of the object when the object is imaged or scanned(for example, the object lying pronely or supinely on the couch when theobject is imaged or scanned), information of an organ of the object,information of a tissue of the object, or the like, or any combinationthereof.

Further, while not shown, the imaging system 100 may be connected to anetwork (e.g., a telecommunications network, a local area network (LAN),a wireless network, a wide area network (WAN) such as the Internet, apeer-to-peer network, a cable network, etc.) for communication purposes.

It should be noted that the above description of the hybrid PET/MRimaging system is merely provided for the purposes of illustration, andnot intended to limit the scope of the present disclosure. For personshaving ordinary skills in the art, multiple variations and modificationsmay be made under the teachings of the present disclosure. For example,in some embodiments, the hybrid PET/MR imaging system may furtherinclude a storage. The storage may store data acquired or generated bythe MR scanner 110, the PET scanner 120, the image processor 130, thecontroller 140, and/or the display 150. As another example, the imagingsystem 100 may utilize SPECT image data as the functional image data.However, those variations and modifications do not depart from the scopeof the present disclosure.

FIG. 2 is a block diagram of the MR scanner 110 according to someembodiments of the present disclosure. As illustrated, the MR scanner110 may include an MR signal acquisition module 210, an MR controlmodule 220, an MR data processing module 230, and an MR data storagemodule 240. The MR signal acquisition module 210 may include a magnetunit 211 and a radio frequency (RF) unit 212. The magnet unit 211 mayinclude a main magnet filed generator and/or a gradient magnet fieldgenerator (not shown in FIG. 2). The main magnet field generator maycreate a static magnetic field BO during a scan of object. The mainmagnet may be of various types including, for example, a permanentmagnet, a superconducting electromagnet, a resistive electromagnet, etc.The gradient magnet field generator may generate magnet field gradientsto the main magnet field BO in the X, Y, and/or Z directions. Thegradient magnet field may encode the spatial information of the objectlocated in the MR scanner 110. The RF unit 212 may include RFtransmitting coils and/or receiving coils. These RF coils may transmitRF signals to or receive RF signals from a region of interest. In someembodiments, the function, size, type, geometry, position, amount,and/or magnitude of the magnet unit 211 and/or of the RF unit 212 may bedetermined or changed according to one or more specific conditions. Forexample, according to the difference in function and size, the RF coilsmay be classified as volume coils and local coils. In some embodimentsof the present disclosure, the volume coils may include birdcage coils,solenoid coil, saddle coil, Helmholtz coil, phased array coil,transverse electro-magnetic coil, loop coil, etc. In some embodiments,the local coils may be phased array coils, and the array may be designedto be used in four-channel mode, eight-channel mode, or 16-channel mode.In some embodiments, the magnet unit 211 and the RF unit 212 may bedesigned to surround the object to form an open low-field magneticresonance imaging scanner or closed magnetic resonance imaging scanner.

The MR control module 220 may control the magnet unit 211 and/or the RFunit 212 of the MR signal acquisition module 210, and/or the MR dataprocessing module 230. The MR control module 220 may receive informationfrom or send pulsed parameters to the MR signal acquisition module 210.The MR control module 220 may control the MR data processing module 230.According to some embodiments of the present disclosure, the MR controlmodule 220 may receive commands from a console provided by, e.g., auser, and adjust the magnet unit 211 and/or RF unit 212 to take imagesof region of interest according to the received commands.

The MR data processing module 230 may process different kinds ofinformation received from different modules. For example, the MR dataprocessing module 230 may process acquired MR data. In some embodiments,the MR data processing module 230 may be a program, an algorithm, and/ora software implemented on the MR control module 220. In someembodiments, the MR data processing module 230 may be an independentsystem, coordinated with the MR control module 220, including aprocesser, a controller, a memory, a display, a program, an algorithm,and/or a software. The signal to be processed may be generated from theMR signal acquisition module 210, or acquired from any storage disclosedanywhere in the present disclosure.

For further understanding of the present disclosure, several examplesare given below, but the examples do not limit the scope of the presentdisclosure. For example, in some embodiments, the MR data processingmodule 230 may process MR signals received from the RF unit 212, and theprocessing may further include channel combination, frequency encoding,phase encoding, or the like, or any combination thereof. The processingmay also include filling the data into the Fourier domain (or referredto as the spatial frequency space or the k-space). Furthermore, the MRdata storage module 240 may store MR data generated by the MR dataprocessing module 230.

It should be noted that the above description of the MR scanner 110 ismerely provided for the purposes of illustration, and not intended tolimit the scope of the present disclosure. For persons having ordinaryskills in the art, multiple variations and modifications may be madeunder the teachings of the present disclosure. For example, in someembodiments, the modules in the MR scanner 110 may include a storageunit respectively. In some embodiments, MR data storage module 240 maybe unnecessary. The MR scanner 110 may share a common storage with anyother modules or components in the imaging system 100. However, thosevariations and modifications do not depart from the scope of the presentdisclosure.

FIG. 3 is a block diagram of the PET scanner 120 according to someembodiments of the present disclosure. The PET scanner 120 may include adetection module 310, a PET signal processing module 320, a coincidencecounting module 330, and a PET control module 340. The PET scanner 120may generate a plurality of PET data during a scan of the object.

In some embodiments, a subject may be located in the PET scanner 120 foran examination, and the subject may be scanned sequentially from thehead to the abdomen and the feet, thereby obtaining images of thesubject. The detection module 310 may include a plurality of detectorcells (not shown in FIG. 3). The detector cells may be arranged in theform of a ring, a part of a ring or cylinder to surround the objectbeing scanned. The detection module 310 may be placed within the wall ofa gantry (not shown in FIG. 3). In some embodiments, PET tracermolecules may be first introduced into the object before an imagingsession begins during a PET scan. The term “PET tracer” or “tracer” asused herein may refer to a substance that may undergo certain changesunder the influence of an activity or functionality within the object,whose activity and/or functionality are to be visualized and/or studiedby the PET. Such changes may be chemical and/or physical, during whichthe PET tracers may emit positrons, namely the antiparticles ofelectrons. A positron may have the same mass and the opposite electricalcharge as an electron, and it may undergo annihilation with an electron(that may naturally exist in abundance within the object) as the twoparticles collide. An electron-positron annihilation may result in twogamma photons with a certain amount of energy (e.g., 511 keV), whichupon their own generation, begin to travel in opposite directions withrespect to one another, the detection module 310 may be designed forgamma photons detection, and generate an electric signal.

The PET signal processing module 320 may generate single event databased on the electric signal generated by the detection module 310. Theterm “single event” as used herein may refer to the detection of a gammaphoton by the detection module 310. A single event may be registeredwhen an annihilation gamma-ray is detected. Single event data mayinclude energy values of the detected annihilation gamma-rays, positioncoordinates for where annihilation gamma-rays are detected, detectiontimes regarding single events, or the like, or any combination thereof.For purposes of illustration, the PET signal processing module 320 mayperform detection time measurement processing, position calculationprocessing, energy calculation processing, or the like, or anycombination thereof. For example, in the detection time measurementprocessing, the PET signal processing module 320 may measure thedetection time when annihilation gamma-rays are detected by thedetection module 310. More specifically, the PET signal processingmodule 320 may monitor the peak value of the electric signal generatedfrom the detection module 310, and register the time when the peak valueof an electric signal exceeds a threshold as a detection time. The PETsignal processing module 320 may detect an annihilation gamma-ray bydetecting when the amplitude of the electric signal exceeds thethreshold. In the position calculation, the PET signal processing module320 may calculate an incident position of annihilation gamma-rays basedon the electric signal that may be generated by the detection module310. In the energy calculation, the PET signal processing module 320 maydetermine an energy value of an annihilation gamma-ray incident based onthe detected electric signal.

The coincidence counting module 330 may process single event datarelating to a plurality of single events. For the purposes ofillustration, the coincidence counting module 330 may determine singleevent data of two single events that fall within a preset time intervalamong supplied single event data. The time interval may be set to, forexample, approximately 6 nanoseconds to 18 nanoseconds. A pair of singleevents detected within the time interval may be deemed to originate froma pair of gamma-rays generated from a same annihilation event. A pair ofsingle events resulting from an annihilation event may be called acoincidence event. The line connecting a pair of the detector cells ofthe coincidence counting module 330 that have detected a coincidenceevent may be called line of response (LOR). The coincidence countingmodule 330 may count coincidence events for each LOR. PET data relatingto coincidence events may be referred to as coincidence event data. Insome embodiments, the PET data may be corrected with respect to randomcoincidences, normalization, dead-time losses, scattering, or the like,or a combination thereof.

Merely by way of example, the PET tracer molecule may be¹⁸F-fluoro-deoxy-glucose (¹⁸F-FDG), which is a radioactive analogue ofglucose. ¹⁸F-FDG may follow a metabolic pathway similar to glucose invivo, but may remain trapped within tissues. Thus, in vivo distributionof ¹⁸F-FDG mapped by PET imaging may indicate glucose metabolicactivity, which may be of interest in oncology as proliferating cancercell may have a higher than average rate of glucose metabolism. Merelyby way of example, the PET tracer molecule may be ¹³N-NH₃ for functionalimaging of myocardial perfusion. In these embodiments, the in vivodistribution of ¹³N-NH₃ may be used to distinguish between viable andnon-viable tissue in poorly perfused areas of the heart of a subject,which may be of interest in cardiology to identify candidates forcoronary by-pass surgery.

Further provided below is a non-exhaustive list of exemplary organic PETtracers that may be used in connection with the present system. In someembodiments, the PET tracer molecules may include ¹¹C-methionine, whichmay act as a marker for protein synthesis in oncology. In someembodiments, the PET tracer molecule may include ¹¹C-flumazenil, whichmay act as a marker for benzodiazepine receptor activity in epilepsy. Insome embodiments, the PET tracer molecule may include ¹¹C-raclopride,which may act as a marker for D2 receptor agonist activity for diagnosisof movement disorders. In some embodiments, the PET tracer molecule mayinclude ¹⁵O-carbon dioxide or ¹⁵O-water, which may act as a marker forblood perfusion in a brain. In some embodiments, the PET tracer mayinclude ¹⁸F-fluoride ion, which may act as a marker for bone metabolismin oncology. In some embodiments, the PET tracer molecule may include¹⁸F fluoro-mizonidazole, which may act as a marker for hypoxia inassessing a subject's response to radiotherapy in oncology. In someembodiments, multiple different PET tracers may be used in combinationto produce complementing sets of functional data.

It should be pointed out that, the MR scanner 110 and the PET scanner120 may be independent of one another in an imaging system. In someembodiments, the MR scanner 110 and the PET scanner 120 may beintegrated into a whole body PET/MR hybrid imaging system, and themagnet unit 211 may be located outside the detection module 310 of thePET scanner 120.

It should be noted that the above description of the PET scanner 120 ismerely provided for the purposes of illustration, and not intended tolimit the scope of the present disclosure. For persons having ordinaryskills in the art, multiple variations and modifications may be madeunder the teachings of the present disclosure. For example, in someembodiments, the PET scanner 120 may further include a storage moduleused to store data generated by the detection module 310, the PET signalprocessing module 320, the coincidence counting module 330, and/or thePET control module 340. In some embodiments, at least some of themodules in the PET scanner 120 may include individual storage units. Insome embodiments, the function of storing data may be realized by theany storage disclosed anywhere in the present disclosure. However, thosevariations and modifications do not depart from the scope of the presentdisclosure.

FIG. 4 is a block diagram of the image processor 130 according the someembodiments of the present disclosure. As illustrated, the imageprocessor 130 may include a storage module 410, a first reconstructionmodule 420, a segmentation module 430, and a second reconstructionmodule 440. The storage module 410 may store different types ofinformation acquired from various devices (e.g., the MR scanner 110,and/or the PET scanner 120, etc.). The information may include anatomicimaging data, molecule imaging data, or the like, or a combinationthereof. In some embodiments, the storage module 410 may store a firsttype of data and a second type of data. The first type of data may beanatomic imaging data acquired from computed tomography (CT) imaging ormagnetic resonance (MR) imaging. The second type of data may be moleculeimaging data acquired from functional imaging including, for example,PET imaging, SPECT imaging, etc. Merely by way of example, the firsttype of data may be MR data, and the second type of data may be PETdata.

The first reconstruction module 420 may reconstruct a first type ofimage of the region of interest (ROI) based on the first type of data.The reconstruction process may include a Fourier transform (FT) of thefirst type of data. The FT may include a fast Fourier Transform (FFT), a2-dimensional FT, a 3-dimensional FT, or the like, or any combinationthereof. In some embodiments, the first type of data may be MR data, andan MR image may be reconstructed based on the MR data.

The segmentation module 430 may segment the first type of image into aplurality of regions (also referred to as a “preliminary segmentation”),and/or generate an attenuation map by assigning initial attenuationcoefficients to voxels of the first type of image. In some embodiments,the method of segmentation may include a thresholding approach, a regiongrowing approach, a histogram approach, an atlas-guided approach, aclustering approach, or the like, or any combination thereof. In someembodiments, the first type of image may include information relating toa bone, a tissue, an organ, a blood vessel, visceral organ, or the like,or any combination thereof. In some embodiments, the plurality ofregions may include at least an unambiguous region and at least anambiguous region. As used herein, an ambiguous region may indicate thatthe values of voxels in the region may be similar to each other, andtherefore the voxels in the region may be undistinguishable from eachother. In some embodiments, the initial attenuation coefficients may beretrieved from a database (not shown in FIG. 4). In some embodiments,the initial attenuation coefficients may be acquired from a user (e.g.,a doctor, etc.). In some embodiments, voxels in different regions may beassigned different initial attenuation coefficients.

In some embodiments, the first type of image may be an MR imagegenerated by the first reconstruction module 420. The MR image mayinclude at least one ambiguous region. The ambiguous region in the MRimage may be an air/bone ambiguous region including air voxels and bonevoxels undistinguishable from each other. The MR image may include atleast one unambiguous region. The unambiguous region in the MR image mayinclude a soft-tissue region including only voxels of a soft-tissue.

In some embodiments, the MR image may be segmented into a plurality ofregions. For instance, the MR image may be segmented into at least threeregions. An attenuation map may be generated by assigning initialattenuation coefficients to voxels of the three regions. The threeregions may include an air/bone ambiguous region and two soft-tissueregions. The two soft-tissue regions may include a first soft-tissueregion corresponding to a first soft tissue type and a secondsoft-tissue region corresponding to a second soft tissue type. The twosoft tissue types may correspond to different attenuation coefficients,respectively. In some embodiments, values of the voxels within asoft-tissue region may be similar to each other. Voxels in a soft-tissueregion may be assigned a same initial attenuation coefficient. In someembodiments, values of the voxels in the air/bone ambiguous region maybe similar to each other. Voxels in the air/bone ambiguous region may beassigned a specific initial attenuation coefficient.

The second reconstruction module 440 may process the second type ofdata. In some embodiments, the second reconstruction module 440 maygenerate a second type of image based on the second type of data. Insome embodiments, the second reconstruction module 440 may update (alsoreferred to as “estimate”) the second type of image based on theattenuation map generated by the segmentation module 430. In someembodiments, the second reconstruction module 440 may update (alsoreferred to as “estimate”) the attenuation map based on the updatedsecond type of image. In some embodiments, the second type of image andthe attenuation map may be iteratively updated (referred to as an“iterative reconstruction process”). In some embodiments, the iterationreconstruction process may include a plurality of iterations, and theattenuation map may be updated after each iteration. In someembodiments, an estimated attenuation map may be generated by after aplurality of iterations in the second reconstruction module 440, and thevoxel of the MR image being segmented may be reassigned attenuationcoefficient based on the estimated attenuation map.

Furthermore, the image processor 130 may segment the first type ofmedical image into a plurality of sub-regions distinguished from eachother. In some embodiments, the image processor 130 may distinguish thebone voxels and the air voxels in the air/bone ambiguous region. Theair/bone ambiguous region may be segmented into a first region and asecond region, the first region includes only the bone voxels and thesecond region includes only the air voxels. In some embodiments, theimage processor 130 may segment the air/bone ambiguous region into afirst region, a second region, and a third region. In some embodiments,the first region includes only voxels of a rib, the second regionincludes only voxels of a spine, and the third region includes onlyvoxels of a lung.

Merely by way of example, the second type of data may be PET dataacquired from the storage module 410 or the PET scanner 120. A firstattenuation map may be generated by assigning attenuation coefficientsto voxels of the MR image. The iteration reconstruction process mayinclude: generating a first PET image based on the PET data;estimating/updating the PET image based on the first attenuation map;estimating/updating the first attenuation map based on the first PETimage, and generating a second attenuation map; updating the first PETimage based on the second attenuation map and generating a second PETimage; repeating the above steps and generating a final attenuation map(also referred to as “the estimated attenuation map”) and a final PETimage (also referred to as “the estimated PET image”).

It should be noted that the above description of the image processor 130is merely provided for the purposes of illustration, and not intended tolimit the scope of the present disclosure. For persons having ordinaryskills in the art, multiple variations and modifications may be madeunder the teachings of the present disclosure. For example, in someembodiments, the storage module 410 may be unnecessary. The function ofthe storage module 410 may be realized by any storage disclosed anywherein the present disclosure. However, those variations and modificationsdo not depart from the scope of the present disclosure.

FIG. 5 is an exemplary flowchart illustrating a process for processingdifferent types of data according to some embodiments of the presentdisclosure. In step 501, acquire a first type of image and a second typeof data. The first type of image may be reconstructed by a first type ofdata. In some embodiments, the first type of data may be anatomicimaging data, and the second type of data may be molecular imaging data.In some embodiments, the first type of medical image may be MR imagereconstructed by MR data, and the first type of medical image mayinclude a plurality of voxels belonging to various organs. The firstreconstruction module 420 may generate the MR image by reconstructingthe MR data which may be send from the storage module 410 or the MRscanner 110. The second type of data may be PET data stored in thestorage module 410, and the PET data may be acquired by the PET scanner120. The PET data may be corrected for random coincidences,normalization, dead-time losses, and scatter. In one embodiment, the MRimage is acquired before the PET scan but after injecting PET tracermolecules into the object. The MR scan may be performed after the PETscan. The MR scan may be performed before injecting PET tracermolecules. The PET tracer may be a single-tracer (e.g., 18F-FDG,18F-EF5, 18F-ML-10, etc.) or dual-tracer (e.g., 18F-FDG and 18F-FLT,11C-ACT and 18F-FDG, etc.) for a PET scanning.

In step 502, execute preliminary segmentation to the first type ofmedical image, and obtain a plurality of regions, wherein the pluralityof regions may include at least an unambiguous region and at least anambiguous region. The ambiguous region may include voxels belonging tovarious organs or tissues, and the voxels belonging to different organsmay be similar to each other according to the pixel values. Theunambiguous region may include voxels belonging to a variety of organ ortissue. In some embodiments, the first type of medical image may be MRimage which may be produced by an MR device, and the MR device mayinclude the MR scanner 110 and the first reconstruction module 420.

In some embodiments, the MR image is preliminarily partitioned orsegmented into a plurality of regions in the segmentation module 430.The plurality of regions may include at least an unambiguous region andat least an ambiguous region. The ambiguous region may be an air/boneambiguous region, the air/bone ambiguous region may include air voxelsand bone voxels undistinguished or mixed from each other, and values ofthe voxels in the air/bone ambiguous region are similar to each other.The unambiguous region may be a soft-tissue region, the soft-tissueregion only includes voxels of soft-tissue, and values of the voxels inthe air/bone ambiguous region may be similar to each other. Thesegmentation of the MR image may include following steps: segment the MRimage into a plurality of soft-tissue regions that only include voxelsof soft-tissue; exclude the voxels of the soft-tissue regions from theMR image, and acquire the air/bone ambiguous region. For persons havingordinary skills in the art, the MR image partition or segmentation mayuse any of variety of the techniques, including but not limited to athresholding approach, a region growing approach, a histogram approach,an atlas-guided approach, or a clustering approach or a combination oftwo or more of the above approaches, etc.

In some embodiments, the MR image may be segmented based on anatlas-guided approach. The procedure may include three stages: a rigidregistration stage, a non-rigid registration stage, and an atlas-guidedsegmentation stage. In some embodiments, the MR image may be segmentedbased on the histogram approach identifying the regions or materialsaccording to the values of voxels. An histogram of voxel datasets overthe regions may be acquired by Formula (1):

h(v)=∫_(x,y,z)δ(f(x,y,z)−v)dxdydz,  (1)

where, v may denote a certain voxel value; f(x,y,z) may denote the valueof a voxel in the MR image; h(v) may denote the number of voxels whosevalues are equal to v; δ may denote a delta function, in which the valueof a voxel is equal to v, δ=1; in other case, δ=0. (x,y,z) may denotethree-dimensional coordinate of a voxel in the plurality of regions, xmay denotes the x-axis coordinate; y may denote the y-axis coordinate,and z may denote the z-axis coordinate. The boundary of materials orregions may be acquired by a resulting voxel histogram.

In some embodiments, the MR image may be partitioned or segmented intoan air/bone ambiguous region, and a plurality of soft-tissue regions.The air/bone ambiguous region may include undistinguishable or mixed airvoxels and bone voxels. In the air/bone ambiguous region, values of thevoxels in the air/bone ambiguous region may be similar. The segmentationof the MR image may include: segmenting the MR image into a plurality ofsoft-tissue regions including only voxels of a soft-tissue; excludingthe soft-tissue regions from the MR image; and acquiring the air/boneambiguous region. The plurality of soft-tissue regions may include afirst soft-tissue region and a second soft-tissue region. Merely by wayof example, the first soft-tissue region may correspond to the liver,and values of the voxels in the first soft-tissue region may be similarto each other or the same. The second soft-tissue region may correspondto the cardiac, and values of the voxels in the second soft-tissueregion may be similar to each other or the same.

In step 503, generate an attenuation map by assigning differentattenuation coefficients to voxels belonging to different regions of thefirst type of image being executed preliminary segmentation. In someembodiments, the first type of image may be MR image that is segmentedinto at least an air/bone ambiguous region, a first soft-tissue region,and a second soft-tissue region as described in step 502. Voxels of theair/bone ambiguous region may be assigned specific attenuationcoefficients. In some embodiments, the specific attenuation coefficientsmay be equal to the attenuation coefficient of water. Voxels of thefirst soft-tissue region may be assigned a first attenuationcoefficient, and voxels of the second soft-tissue region may be assigneda second attenuation coefficient. The first attenuation coefficient maybe different from the second attenuation coefficient. For instance, thefirst attenuation coefficient may be equal to the attenuationcoefficient of the liver, and second attenuation coefficienth may beequal to the attenuation coefficient of the cardiac. In someembodiments, the step 502 and the step 503 may be executed in thesegmentation module 430.

Iteratively reconstruct the second type of data and the attenuation mapto generate a second type of medical image and an estimated attenuationmap, or iteratively reconstruct a second type of data based on theattenuation map; the attenuation map may be updated or estimated aftereach iteration. An estimated attenuation map may be acquired viaiteratively updating/estimating or reconstructing the attenuation mapduring the iterative reconstruction of the second type of data. In someembodiments, an estimated attenuation map and a second type of medicalimage may be obtained by iteratively reconstruct the attenuation map anda second type of data simultaneously, wherein the attenuation map may beupdated after each iteration. The iteration may contain updating orreconstructing the second type of data in step 504 and updating orreconstructing the attenuation map in step 505. In step 504, the secondtype of data may be updated or reconstructed based on the attenuationmap. In step 505, the attenuation map may be updated or reconstructedbased on the second type of data being updated. In some embodiments, theattenuation map may be a first attenuation map as described in step 503,and the first attenuation map may be updated to generate a secondattenuation map. It should be noted that, the terms “reconstruct”,“estimate” and “update”, when used for the generation of the second typeof medical image in this disclosure, may represent a similar process ofthe second type of data or image correction. In some embodiments, theterms “reconstruct”, “estimate” and “update”, when used for thegeneration of the estimated attenuation map in this disclosure, mayrepresent a similar process of the attenuation coefficients correction.

In some embodiments, the first attenuation map may be continuouslyupdated during a reconstruction of the second type of data in the secondreconstruction module 440. The second type of data may be PET datagenerating from the PET scanner 120. The reconstruction of the PET datamay be based on a time-of-flight (TOF) technique. A detection timedifference of a pair of annihilation gamma-rays may be measured and/orrecorded by a TOF based method. The probability of presence of the pairannihilation point in each voxel on the line of response (LOR) may bedifferent depending on the detection time difference of coincidenceevents. For example, TOF-PET scan may measure the time differencebetween the detection of two 511 keV annihilation photons. For thepurposes of illustration, taking a reconstruction of the PET scanner 120for example, the PET data may be updated based on maximum a posteriori(MAP) method in the second reconstruction module 440. A firstattenuation map may be generated by assigned attenuation coefficients tovoxels of the first type of image being executed preliminary in thesegmentation module 430. The process of iteration in the secondreconstruction module 440 may include: updating the PET data based onthe first attenuation map, and generating a first PET image; thenupdating the first attenuation map based on the PET data of the firstPET image, and generating a second attenuation map. In some embodiments,a second attenuation map may be generated to obtain a third attenuationmap. The third attenuation map may be an estimated attenuation mapcorresponding to a final PET image. The final PET image may be a PETimage attenuation corrected by the estimated attenuation map. In someembodiments, the reconstruction of a PET data or the acquisition of theestimated attenuation map may need many times of iterations, theestimated PET image and the estimated attenuation map may be obtainedafter the final iteration.

In step 506, reassign attenuation coefficients to the voxels of thefirst type of medical image based on the estimated or final attenuationmap. In some embodiments, the voxels of the ambiguous regions may be anair/bone ambiguous region in a MR image which has been executedpreliminary segmentation as described in step 502, and the air/boneambiguous region includes air voxels and bone voxels undistinguishedfrom each other. The attenuation map may be updated by step 505 morethan once. An estimated attenuation map may be acquired after multipleiterations. The updated attenuation coefficients of air voxels may bedifferent from the updated attenuation coefficients of bone voxels inthe estimated attenuation map. The bone voxels and air voxels in theair/bone ambiguous region can be distinguished based on the estimatedattenuation map.

Furthermore, segmenting the first type of medical image into a pluralityof sub-regions distinguished from each other. The sub-region may onlycontain voxels belonging to a variety of organ or tissue. The ambiguousregion may be segmented into at least two sub-regions which may belongto different organs or tissues.

FIG. 6 is another exemplary flowchart illustrating a process forprocessing different types of data according to some embodiments of thepresent disclosure. In step 602, a first type of data (also referred toas “first data”) and a second type of data (also referred to as “seconddata”) may be acquired. The first type of data and the second type ofdata may be acquired real time or retrieved from any storage disclosedanywhere in the present disclosure. In some embodiments, the first typeof data may be anatomic imaging data, and the second type of data may bemolecular imaging data or functional imaging data. Merely by way ofexample, in a hybrid PET/MR system, the first type of data may be MRdata and the second type of data may be PET data. The MR data may beacquired by the MR scanner 110, and the PET data may be acquired by thePET scanner 120. The PET data may be corrected with respect to, forexample, random coincidences, normalization, dead-time losses,scattering, etc.

In some embodiments, the first type of data and the second type of datamay be acquired based on a first scan (e.g., an MR scan) and a secondscan (a PET scan) simultaneously or successively in any order. Forexample, the MR scan may be performed before the PET scan but afterinjecting PET tracer molecules into the object. As another example, theMR scan may be performed after the PET scan. As a further example, theMR scan may be performed before injecting PET tracer molecules. In someembodiments, the PET tracer may be a single-tracer (e.g., 18F-FDG,18F-EF5, 18F-ML-10, etc.) or dual-tracer (e.g., 18F-FDG and 18F-FLT,11C-ACT and 18F-FDG, etc.) for a PET scanning.

In step 604, a first type of image (also referred to as “first image”)may be generated based on the first type of data. The generation of thefirst type of image may be performed by the first reconstruction module420. For example, in the hybrid PET/MR system, the first type of imagemay be an MR image reconstructed based on the MR data. The first type ofimage may include a plurality of voxels corresponding to various organsor tissues.

In step 606, the first type of image may be segmented into a pluralityof regions. The plurality of regions may include at least an unambiguousregion and at least an ambiguous region. The segmentation may beperformed by the segmentation module 430. As used herein, an ambiguousregion may refer to that values of voxels in the region may be similarto each other, and therefore the voxels in the region may beundistinguishable from each other. In some embodiments, the ambiguousregion may include voxels corresponding to various organs or tissues,and the voxels corresponding to different organs may beundistinguishable from each other according to similar voxel values. Theunambiguous region may include voxels corresponding to a specific organor tissue.

Merely by way of example, in some embodiments, the MR image may besegmented (also referred to as “partitioned”) into a plurality ofregions. The plurality of regions may include at least an unambiguousregion and at least an ambiguous region. The ambiguous region may be anair/bone ambiguous region. The air/bone ambiguous region may include airvoxels and bone voxels undistinguishable from each other due to similarvoxel values. The unambiguous region may be a soft-tissue region. Thesoft-tissue region may include voxels of a soft-tissue. Values of thevoxels in the soft-tissue region may be similar to each other. Thesegmentation of the MR image may include the following steps: segmentingthe MR image into a plurality of soft-tissue regions that only includevoxels of a soft-tissue; excluding the voxels of the soft-tissue regionsfrom the MR image; and acquiring the air/bone ambiguous region. Forpersons having ordinary skills in the art, during the segmentation ofthe MR image, a variety of techniques may be used, including athresholding approach, a region growing approach, a histogram approach,an atlas-guided approach, or a clustering approach, or a combination oftwo or more of the above approaches, etc.

In some embodiments, the MR image may be segmented based on anatlas-guided approach. The procedure may include three stages: a rigidregistration stage, a non-rigid registration stage, and an atlas-guidedsegmentation stage. In some embodiments, the MR image may be segmentedbased on the histogram approach identifying the regions or materialsaccording to the values of voxels, and the histogram of voxel datasetsover the regions may be acquired by the Formula (1).

In some embodiments, the MR image may be partitioned or segmented intoan air/bone ambiguous region, and a plurality of soft-tissue regions.The air/bone ambiguous region may include undistinguishable or mixed airvoxels and bone voxels. In the air/bone region, values of the voxels maybe similar. The segmentation of the MR image may include: segmenting theMR image into a plurality of soft-tissue regions including only voxelsof a soft-tissue; excluding the soft-tissue regions from the MR image;and acquiring the air/bone ambiguous region. The plurality ofsoft-tissue regions may include a first soft-tissue region and a secondsoft-tissue region. Merely by way of example, the first soft-tissueregion may correspond to the liver, and values of the voxels in thefirst soft-tissue region may be similar to each other or the same. Thesecond soft-tissue region may correspond to the heart, and values of thevoxels in the second soft-tissue region may be similar to each other orthe same.

In step 608, an attenuation map may be generated by assigning differentattenuation coefficients to voxels belonging to different regions of thefirst type of image being executed segmentation. In some embodiments,the first type of image may be MR image that is segmented into at leastan air/bone ambiguous region, a first soft-tissue region, and a secondsoft-tissue region as described in step 606. Voxels of the air/boneambiguous region may be assigned specific attenuation coefficients. Insome embodiments, the specific attenuation coefficients may be equal tothe attenuation coefficient of water. Voxels of the first soft-tissueregion may be assigned a first attenuation coefficient, and voxels ofthe second soft-tissue region may be assigned a second attenuationcoefficient. The first attenuation coefficient may be different from thesecond attenuation coefficient. For instance, the first attenuationcoefficient may be equal to the attenuation coefficient of the liver,and second attenuation coefficienth may be equal to the attenuationcoefficient of the cardiac. In some embodiments, the step 606 and thestep 608 may be executed in the segmentation module 430.

In step 610, a second type of image (also referred to as “second image”)may be generated based on the second type of data. In step 612, thesecond type of image may be updated or reconstructed based on the(updated) attenuation map. In step 614, the attenuation map may beupdated or reconstructed based on the updated second type of image. Step610, step 612, and step 614 may be performed by the secondreconstruction module 440. In some embodiments, the second type of imageand the attenuation map may be updated iteratively. As used herein, theupdating the second type of image and the attenuation map may be alsoreferred to as an “iterative reconstruction process.” In someembodiments, the iterative reconstruction process may include aplurality of iterations. The second type of image and the attenuationmap may be updated after each iteration. In some embodiments, anestimated attenuation map and an estimated second type of image may beobtained.

In some embodiments, the second type of image may be a PET image. Thereconstruction of the PET image may be based on a time-of-flight (TOF)technique. A difference of detection time of a pair of annihilationgamma-rays may be measured and/or recorded by a TOF based method. Theprobability of presence of the pair annihilation point in each voxel onthe line of response (LOR) may be different depending on the differenceof detection time of coincidence events. For example, TOF-PET scan maymeasure the time difference between the detection of two 511 keVannihilation photons. For the purposes of illustration, taking areconstruction of the PET image for example, the PET image may beupdated based on maximum a posteriori (MAP) method by the secondreconstruction module 440. Merely by way of example, a first attenuationmap may be generated by assigning attenuation coefficients to voxels ofthe first type of image on which segmentation is being executed. Theiteration reconstruction process may include: updating the PET imagebased on the first attenuation map, and generating a first PET image;updating the first attenuation map based on the first PET image, andgenerating a second attenuation map. In some embodiments, a second PETimage may be generated based on the second attenuation, and a thirdattenuation map may be generated based on the second PET image, or thelike. The third attenuation map may be referred to as an estimatedattenuation map corresponding to a final PET image (also may be referredto as an “estimated PET image”). The final PET image may be a PET imageattenuation corrected by the estimated attenuation map. In someembodiments, the iteration reconstruction process may include aplurality of iterations, and the estimated PET image and the estimatedattenuation map may be obtained after the final iteration.

In step 616, the ambiguous region may be segmented into a plurality ofsub-regions based on the estimated attenuation map. The segmentation maybe performed by the second reconstruction module 440. The sub-region mayonly include voxels belonging to a specific organ or tissue. Theambiguous region may be segmented into at least two sub-regions that maycorrespond to different organs or tissues. In some embodiments, thevoxels of the ambiguous regions may be an air/bone ambiguous region inan MR image on which segmentation has been executed as described in step606. The air/bone ambiguous region may include air voxels and bonevoxels undistinguishable from each other. As described in step 614, theattenuation map may be updated more than once. An estimated attenuationmap may be acquired after multiple iteration steps. The updatedattenuation coefficients of air voxels may be different from the updatedattenuation coefficients of bone voxels in the estimated attenuationmap. The bone voxels and the air voxels in the air/bone ambiguous regionmay be distinguished based on the estimated attenuation map.

It should be noted that, the terms “reconstruct,” “estimate,” and“update,” when used for the generation of the second type of image inthis disclosure, may represent a similar process for correcting orotherwise acquiring the second type of data or image. In someembodiments, the terms “reconstruct,” “estimate,” and “update,” whenused for the generation of the estimated attenuation map in thisdisclosure, may represent a similar process for correcting or otherwiseacquiring the attenuation coefficients.

FIG. 7 is an exemplary flowchart illustrating a process for segmenting amedical image according to some embodiments of the present disclosure.In step 701, a MR image and PET data indicative of a object may beacquired during a scan of object using a hybrid PET/MR apparatus, andthe hybrid PET/MR apparatus may include a MR device (imaging system) anda PET device (imaging system). In some embodiments, the MR device mayinclude a MR scanner 110 and a first reconstruction module 420, the MRscanner 110 may generate a plurality of MR data during a scan of anobject, and the reconstruction module 420 may generate an MR image byreconstruction the plurality of MR data. The PET device may include aPET scanner 120 which may generate a plurality of PET data during a scanof the object. The MR image may be acquired before or after theacquisition of the PET data. Furthermore, the PET device may include asecond reconstruction module 440, which may be used for acquiring a PETimage via reconstructing the PET data.

In step 702, execute preliminary segmentation to the MR image, the MRimage may be segmented into a plurality of regions which includes atleast an air/bone ambiguous region, the air/bone ambiguous regionincludes indistinguishable or mixed air voxels and bone voxels. Forpersons having ordinary skills in the art, the MR image partition mayuse any of variety of the techniques, including but not limited to athresholding approach, a region growing approach, a histogram approach,an atlas-guided approach, or a clustering approach or combination of oneor more of the above approaches with empirical reasoning, etc. In someembodiments, the MR image may be preliminarily partitioned or segmentedinto an air/bone ambiguous region and a plurality of soft-tissue regionsin the segmentation module 430. The air/bone ambiguous region mayinclude air voxels and bone voxels undistinguished or mixed from eachother, and values of the voxels in the air/bone ambiguous region may besimilar. The plurality of soft-tissue regions may include a firstsoft-tissue region and the second soft-tissue region. The firstsoft-tissue region may corresponds to the cardiac, and values of thevoxels in the first soft-tissue region may be similar or the same. Thesecond soft-tissue region may correspond to the fat, and values of thevoxels in the second soft-tissue region may be similar or the same. Theattenuation coefficients corresponding to the cardiac may be differentfrom the attenuation coefficients corresponding to the fat.

In step 703, generate an attenuation map by assigning attenuationcoefficients to voxels of the plurality of regions as described in step702. The air voxels and the bone voxels in the air/bone ambiguous regionmay be assigned to specific radiation attenuation values, and thespecific radiation attenuation values may be equal to attenuationcoefficient of water. For example, as illustrated in FIG. 9, voxels ofthe region A are assigned to the attenuation coefficient of water,voxels of the region B are assigned to attenuation coefficientscorresponding to the cardiac or heart, and voxels of the region C areassigned to attenuation coefficients corresponding to the fat. In someembodiments, the step 702 and the step 703 may be executed in thesegmentation module 430.

Iteratively reconstruct the PET data based on the attenuation map, andthe attenuation map is updated after each iteration of the iterativelyreconstruction. An estimated attenuation map may be acquired viaiteratively updating or reconstructing the attenuation map during theiterative reconstruction of the PET data. The iteration may containupdating or reconstructing the PET data in step 704 and updating orreconstructing the attenuation map in step 705. In step 704, the PETdata may be updated or reconstructed based on the attenuation map. Instep 705, the attenuation map may be updated or reconstructed based onthe updated PET data. In some embodiments, the attenuation map asdescribed in step 703 may be a first attenuation map, and the firstattenuation map may be updated to generate a second attenuation map. Insome embodiments, an estimated attenuation map and a PET image may beobtained by iteratively reconstructing the attenuation map and the PETdata simultaneously, and the attenuation map may be updated or estimatedafter each iteration. It should be noted that, the terms “reconstruct”,“estimate” and “update”, when used for the generation of the PET imagein this disclosure, may represent a similar process of the PET data orPET image correction. In some embodiments, the terms “reconstruct”,“estimate” and “update”, when used for the generation of the estimatedattenuation map in this disclosure, may represent a similar process ofthe attenuation coefficients correction.

In some embodiments, the first attenuation map may be continuously oriteratively updated during a reconstruction of PET data. Thereconstruction of the PET data may be based on a time-of-flight (TOF)technique. A detection time difference of a pair of annihilationgamma-rays may be measured and/or recorded by a TOF based method. Theprobability of presence of the pair annihilation point in each voxel onthe line of response (LOR) may be different depending on the detectiontime difference of coincidence events. For example, TOF-PET scan maymeasure the time difference between the detection of two 511 keVannihilation photons. For the purposes of illustration, taking areconstruction of the PET data acquired from the PET scanner 120 forexample, the PET data acquired from the PET scanner 120 may be updatedbased on maximum a posteriori (MAP) method as expressed in Formula (2)below:

$\begin{matrix}{{f_{j}^{({n,m})} = {{\max\limits_{f}{y_{i}^{t}{\log \left( {{{\overset{\_}{a}}_{i}^{({n,m})}{\sum_{t,{i \in S_{m}}}{H_{ijt}f_{j}}}} + {s_{i}(t)} + {r_{i}(t)}} \right)}}} - \left( {{{\overset{\_}{a}}_{i}^{({n,m})}{\sum_{it}{H_{ijt}x_{j}}}} + {s_{i}(t)} + {r_{i}(t)}} \right) - {U(f)}}},} & (2)\end{matrix}$

where f_(j) ^((n,m)) may denote the PET data or image acquired in then-th iteration to the m-th sub-iteration of subset; S_(m) may denote am-th data subset in data space; H_(ijt) may denote a system matrixincluding TOF information; i may denote the index number of LOR; j maydenote the j-th voxel of PET data or image; t may denote the indexnumber of TOF bin; s_(i)(t) may denote the number of scatteringcoincidence events on an i-th LOR of a t-th TOF bin; r_(i)(t) may denotethe number of random coincidence events on an i-th LOR of a t-th TOFbin; y_(i) ^(t) may denote the number of counts on a i-th LOR of a t-thTOF bin; U(f) may denote a smoothing term of the PET data or image inimage domain; ā_(i) ^((n,m)) may denote the i-th element of anattenuation sinogram, and the attenuation sinogram may be acquired by am-th sub-iteration of a subset after n iterations. The value of ā_(i)^((n,m)) may be determined with a formula of:

ā _(i) =e ^(−Σ) ^(j) ^(l) ^(ij) ^(ρ) ^(j) ,  (3)

where, l_(i,j) may denote a system matrix of a line integral; ρ_(j) maydenote attenuation coefficient of a voxel j; and the initial value ofρ_(j) may be equal to attenuation coefficient of water (0.096). For avoxel of a soft-tissue region, the value of ā_(i) ^((n,m)) may bedetermined with a formula of:

ā _(i) =e ^(−Σ) ^(k=1) ^(M) ^(μ) ^(k) ^(Σ) ^(j) ^(l) ^(ij) ^(I) ^(k)^((j)),  (4)

where I_(k)(j) may denote a pixel value of a voxel j.In some embodiments, for a voxel of a soft-tissue region or an air/boneambiguous region, the value of ā_(i) ^((n,m)) may be determined with theunified formula of:

ā _(i) =e ^(−Σ) ^(k=1) ^(M) ^(μ) ^(k) ^(Σ) ^(j) ^(l) ^(ij) ^(I) ^(k)^((j)) e ^(−Σ) ^(j) ^(l) ^(ij) ^(ρ) ^(j) ^((1−Σ) ^(k=1) ^(M) ^(I) ^(k)^((j))),  (5)

where, M is the number of soft-tissue regions in the MR image; μ_(k) maydenote an attenuation coefficient of one soft-tissue region (1≤k≤M);l_(i,j) may denote a system matrix of a line integral; i may denote theindex number of LOR; ρ_(j) may denote specific attenuation coefficientof a voxel j; the initial value of ρ_(i) may be equal to attenuationcoefficient of water (0.096); and I_(k)(j) may denote a pixel value of avoxel j, the value of I_(k)(j) may be set to one when the voxel is inthe k-th soft-tissue region, and in other situations, the value ofI_(k)(j) may be set to zero.

An expectation of a voxel of a non-TOF sinogram may be determined with aformula of:

y _(i) ^((n,m)) =ā _(i) ^((n,m))Σ_(j,t) H _(ijt) f _(j) ^((n,m)),  (6)

where y _(i) ^((n,m)) may denote an expectation of a voxel of a non-TOFsinogram; the voxel may be one voxel of the PET image, which may havebeen performed a n-th iteration and a (m)-th sub-iteration.

The attenuation map may be updated based on the updated PET data, anupdated attenuation coefficient of the air/bone ambiguous region may bedetermined with a formula of:

$\begin{matrix}{{\rho_{j}^{({n,{m + 1}})} = {\rho_{j}^{({n,m})} + \frac{\sum_{i \in S_{m}}{l_{ij}\frac{{\overset{\_}{y}}_{i}^{({n,m})}}{{\overset{\_}{y}}_{i}^{({n,m})} + s_{i} + r_{i}}\left( {{\overset{\_}{y}}_{i}^{({n,m})} + s_{i} + r_{i} - y_{i}} \right)}}{\sum_{i \in S_{m}}{l_{ij}\; \frac{\left( {\overset{\_}{y}}_{i}^{({n,m})} \right)^{2}}{{\overset{\_}{y}}_{i}^{({n,m})} + s_{i} + r_{i\;}}{\sum_{k}l_{ik}}}}}},} & (7)\end{matrix}$

where ρ_(j) ^((n,m+1)) may denote a second attenuation coefficient ofthe j-th voxel in the second attenuation map, which may be updated fromρ_(j) ^((n,m)), ρ_(j) ^((n,m)) may denote a first attenuationcoefficient acquired after performing a n-th iteration to a m-thsub-iteration of the j-th voxel, l_(ij) may denote a system matrix of aline integral, referring to the length of the i-th LOR through a voxelj, which may be generated from an attenuation map corresponding to anattenuation coefficient; s_(i) may denote the number of scatteringcoincidence events acquired from the i-th LOR; r_(i) may denote thenumber of random coincidence events acquired from the i-th LOR; and y_(i) ^((n,m)) may denote an expectation of i-th voxel of a local PETimage corresponding to the air/bone ambiguous region in TOF sinogram,where the local PET image may have been performed a n-th iteration and a(m)-th sub-iteration in the second reconstruction module 440.

An updated or estimated attenuation coefficient of the soft-tissueregions may be determined with a formula of:

$\begin{matrix}{{\mu_{p}^{({n,{m + 1}})} = {\mu_{p}^{({n,m})} + \frac{\sum_{i \in S_{m}}{\frac{{\overset{\_}{y}}_{i}^{({n,m})}}{{\overset{\_}{y}}_{i}^{({n,m})} + s_{i} + r_{i}}\left( {{\overset{\_}{y}}_{i}^{({n,m})} + s_{i} + r_{i} - y_{i}} \right){\sum_{j}{l_{ij}{I_{p}(j)}}}}}{\sum_{i \in S_{m}}{l_{ij}\; \frac{\left( {\overset{\_}{y}}_{i}^{({n,m})} \right)^{2}}{{\overset{\_}{y}}_{i}^{({n,m})} + s_{i} + r_{i\;}}\left( {\sum_{k}{l_{ik}{I_{p}(j)}}} \right){\sum_{k}l_{ik}}}}}},} & (8)\end{matrix}$

where μ_(p) ^((n,m+1)) may denote a second attenuation coefficient ofthe p-th soft-tissue region in the second attenuation map, which may beupdated from μ_(p) ^((n,m)); μ_(p) ^((n,m)) may denote a firstattenuation coefficient acquired after performing a n-th iteration to am-th sub-iteration of the p-th soft-tissue region; l_(ij) may denote asystem matrix of a line integral, referring to the length of the i-thLOR through a voxel j, which may be generated from an attenuation mapcorresponding to an attenuation coefficient; s_(i) may denote the numberof scattering coincidence events acquired from the i-th LOR; r_(i) maydenote the number of random coincidence events acquired from the i-thLOR; y _(i) ^((n,m)) may denote an expectation of i-th voxel of the PETimage in TOF sinogram, the PET image may have been performed a n-thiteration and a (m)-th sub-iteration.

In some embodiments, one or more iterations may be performed, and anestimated attenuation map may be acquired via iteratively updating theattenuation map during the iterative reconstruction of the second typeof data. In some embodiments, keeping an attenuation map (also referredto as a first attenuation map) fixed, an iteration may be performed to aPET data to acquire a PET image, and then keeping the PET image fixed,an iteration may be performed to the first attenuation map to acquire asecond PET attenuation image. For one iteration, each subset in dataspace corresponding to the first attenuation map may be traversed. Then,another iterations may be performed until a criterion is satisfied andthe iteration may terminate, the final attenuation map may be theestimated attenuation map. The iterations of the PET data or thegeneration of the estimated attenuation map may be processed in thesecond reconstruction module 440.

In step 706, reassign attenuation coefficients to each voxel of an MRimage based on the estimated attenuation map, and distinguish the bonevoxels and air voxels in the air/bone ambiguous region. The attenuationcoefficient of air being updated is different from the attenuationcoefficient of bone being updated, and the bone voxels and air voxels inthe air/bone ambiguous region can be distinguished based on the updatedattenuation map. Furthermore, segment the MR image into a plurality ofsub-regions distinguished from each other, wherein the air/boneambiguous region may be segmented into a first sub-region and a secondsub-region, the first sub-region may only include the bone voxels, andthe second sub-region may only include the air voxels.

FIG. 8 is another exemplary flowchart illustrating a process forsegmenting an image according to some embodiments of the presentdisclosure. In step 802, MR data and PET data may be acquired. In someembodiments, the MR data and the PET data may be acquired during a scanof an object by a hybrid PET/MR apparatus (e.g., the imaging system100). The hybrid PET/MR apparatus may include an MR device (e.g., the MRscanner 110), a PET device (e.g., the PET scanner 120), and a processingdevice (e.g., the image processor 130. In some embodiments, the MR dataand PET data may be retrieved from a storage disclosed anywhere in thepresent disclosure. In some embodiments, the MR data and the PET datamay be acquired simultaneously or successively.

In step 804, an MR image may be generated based on the MR data. In someembodiments, the MR image may be generated by the first reconstructionmodule 420. In some embodiments, the MR image may be generated before orafter the acquisition of the PET data.

In step 806, the MR image may be segmented (also referred to as“partitioned”) into a plurality of regions including at least anair/bone ambiguous region. The segmentation may be performed by thesegmentation module 430. For persons having ordinary skills in the art,during the segmentation of the MR image, a variety of techniques may beused, including a thresholding approach, a region growing approach, ahistogram approach, an atlas-guided approach, a clustering approach, ora combination of two or more of the above approaches, etc.

In some embodiments, the MR image may be segmented into an air/boneambiguous region and one or more soft-tissue regions. The air/boneambiguous region may include air voxels and bone voxelsundistinguishable from each other, and values of the voxels in theair/bone ambiguous region may be similar or the same. The one or moresoft-tissue regions may include a first soft-tissue region and a secondsoft-tissue region. The first soft-tissue region may correspond to theheart of the subject, and values of the voxels in the first soft-tissueregion may be similar or the same. The second soft-tissue region maycorrespond to fat, and values of the voxels in the second soft-tissueregion may be similar or the same. In some embodiments, the attenuationcoefficients corresponding to the heart may be different from theattenuation coefficients corresponding to the fat. For example, asdescribed in FIG. 9, an MR image regarding the head may be segmentedinto three regions including region A (air/bone ambiguous region),region B corresponding to the heart, and region C corresponding to thefat.

In step 808, an attenuation map may be generated by assigningattenuation coefficients to voxels of the plurality of regions asdescribed in step 806. The attenuation map may be generated by thesegmentation module 430. In some embodiments, the air voxels and thebone voxels in the air/bone ambiguous region may be assigned to aspecific attenuation coefficient. In some embodiments, the specificattenuation coefficient may be equal to the attenuation coefficient ofwater. For example, as illustrated in FIG. 9, voxels of the region A areassigned the attenuation coefficient of water, voxels of the region Bare assigned the attenuation coefficients corresponding to the heart,and voxels of the region C are assigned the attenuation coefficientscorresponding to the fat.

In step 810, a PET image may be generated based on the PET data. In someembodiments, the PET image may be generated by the second reconstructionmodule 440. As used herein, the PET image may be an initial imagegenerated based on the PET data. In step 812, the PET image may beupdated based on the attenuation map. In step 814, the attenuation mapmay be updated based on the updated PET image. Subsequently, the PETimage may be updated based on the updated attenuation map. In someembodiments, the PET image and the attenuation map may be updatediteratively. In some embodiments, an estimated attenuation map and anestimated PET image may be acquired. As used herein, the updating thePET image and the attenuation map may be also referred to as an“iterative reconstruction process.” In some embodiments, the iterationreconstruction process may include a plurality of iterations, and theattenuation map may be updated after each iteration.

In some embodiments, the PET image may be generated based on atime-of-flight (TOF) technique. A difference of detection time between apair of annihilation gamma-rays may be measured and/or recorded by a TOFbased method. The probability of presence of the pair of annihilationpoint in each voxel on the line of response (LOR) may be differentdepending on the difference of detection time of coincidence events. Forexample, TOF-PET scan may measure the time difference between thedetection of two 511 keV annihilation photons. For the purposes ofillustration, the PET image may be updated based on maximum a posteriori(MAP) method as expressed in the Formula (2).

In step 814, the attenuation map may be updated based on the updated PETimage. In some embodiments, the tissue regions and the air/boneambiguous regions in the attenuation map may be updated respectively.For example, the air/bone ambiguous region may be updated expressed inthe Formula (7), and the attenuation coefficients of a soft-tissueregion may be updated expressed in the Formula (8).

In some embodiments, one or more iterations may be performed, and anestimated attenuation map may be acquired via iteratively updating theattenuation map during the iterative reconstruction process. In aniteration, both the PET image and the attenuation map may be updated.Merely by way of example, keeping an attenuation map (also referred toas a first attenuation map) fixed, a round of reconstruction may beperformed to the PET data to acquire a PET image, and then keeping thePET image fixed, the first attenuation map may be updated to acquire asecond PET attenuation image. During one iteration, each subset in dataspace corresponding to the first attenuation map may be traversed and/orupdated. Then, one or more iterations may be performed until a criterionis satisfied and the iteration may terminate, a final attenuation mapmay be generated (also referred to as “the estimated attenuation map”).Exemplary criteria may include the number of iterations to be performed,the change in a value (e.g., the PET image, the attenuation map, etc.)in a number of iterations lower than a threshold, or the like, or acombination thereof.

In step 816, the bone voxels and the air voxels in the air/boneambiguous region may be distinguished based on the updated attenuationmap (also referred to as the “estimated attenuation map”). For example,voxels in the MR image may be reassigned attenuation coefficients basedon the estimated attenuation map. The attenuation coefficients of airbeing updated is different from the attenuation coefficients of bonebeing updated, and the bone voxels and air voxels in the air/boneambiguous region may be distinguished based on the updated attenuationmap or their respective attenuation coefficients. Furthermore, the MRimage may be segmented into a plurality of sub-regions distinguishedfrom each other. For example, the air/bone ambiguous region may besegmented into a first sub-region including bone voxels and a secondsub-region including air voxels.

It should be noted that, the terms “reconstruct,” “estimate,” and“update,” when used for the generation of the PET image in thisdisclosure, may represent a similar process for correcting or otherwiseacquiring the PET data or PET image. In some embodiments, the terms“reconstruct,” “estimate,” and “update,” when used for the generation ofthe estimated attenuation map in this disclosure, may represent asimilar process for correcting or otherwise acquiring the attenuationcoefficients.

EXAMPLES

The following examples are provided for illustration purposes only, andnot intended to limit the scope of the present disclosure.

Example 1

FIG. 9 illustrates a preliminary segmentation of an MR image. The MRimage shown here is the transversal view of the torso. Two levels ofgrey scale values may be seen in FIG. 9. The MR image was preliminarilysegmented into region A, region B, and region C. The region B mayinclude voxels of the heart with a similar grey value, and the region Cmay include voxels of fat with a similar grey value. The region A mayinclude voxels belonging to different organs, and the voxels weredifficult to distinguish due to similar voxel values. Region A mayinclude voxels of the spine, voxels of a rib, voxels of a lung, orvoxels of air.

Example 2

FIG. 10 illustrates an estimated attenuation map generated according tosome embodiments of the present disclosure. At least three levels ofgrey scale values may be seen in FIG. 10. Different portions of theestimated attenuation map had different grey scale values (or differentattenuation coefficients). The grey scale values of the spine and thegrey scale values of the ribs were higher than the grey scale values ofother regions in the estimated attenuation map, indicating the estimatedor updated attenuation coefficients corresponding to the spine or ribswere higher than the estimated or updated attenuation coefficientscorresponding to the soft tissue(s). The grey scale values correspondingto the lung were lower than the grey scale values corresponding to otherregions in the estimated attenuation map, indicating the estimated orupdated attenuation coefficients corresponding to the lung were lowerthan the estimated or updated attenuation coefficients corresponding tothe soft tissues. Simple thresholding segmentation according toattenuation coefficients may lead to separation of lungs and bones. TheMR image may be segmented into a plurality of sub-regions includingregion A1, region A2, region A3, region A4, region B, and region C. Theregion A1 may include only voxels of a lung or air, the regions A2 andA3 may include only voxels of the ribs, and the regions A4 may includeonly voxels of the spine.

The various methods and techniques described above provide a number ofways to carry out the application. Of course, it is to be understoodthat not necessarily all objectives or advantages described can beachieved in accordance with any particular embodiment described herein.Thus, for example, those skilled in the art will recognize that themethods may be performed in a manner that achieves or optimizes oneadvantage or group of advantages as taught herein without necessarilyachieving other objectives or advantages as taught or suggested herein.A variety of alternatives are mentioned herein. It is to be understoodthat some preferred embodiments specifically include one, another, orseveral features, while others specifically exclude one, another, orseveral features, while still others mitigate a particular feature byinclusion of one, another, or several advantageous features.

Furthermore, the skilled artisan will recognize the applicability ofvarious features from different embodiments. Similarly, the variouselements, features and steps discussed above, as well as other knownequivalents for each such element, feature or step, may be employed invarious combinations by one of ordinary skill in this art to performmethods in accordance with the principles described herein. Among thevarious elements, features, and steps some will be specifically includedand others specifically excluded in diverse embodiments.

Although the application has been disclosed in the context of certainembodiments and examples, it will be understood by those skilled in theart that the embodiments of the application extend beyond thespecifically disclosed embodiments to other alternative embodimentsand/or uses and modifications and equivalents thereof.

Preferred embodiments of this application are described herein.Variations on those preferred embodiments will become apparent to thoseof ordinary skill in the art upon reading the foregoing description. Itis contemplated that skilled artisans may employ such variations asappropriate, and the application may be practiced otherwise thanspecifically described herein. Accordingly, many embodiments of thisapplication include all modifications and equivalents of the objectmatter recited in the claims appended hereto as permitted by applicablelaw. Moreover, any combination of the above-described elements in allpossible variations thereof is encompassed by the application unlessotherwise indicated herein or otherwise clearly contradicted by context.

All patents, patent applications, publications of patent applications,and other material, such as articles, books, specifications,publications, documents, things, and/or the like, referenced herein arehereby incorporated herein by this reference in their entirety for allpurposes, excepting any prosecution file history associated with same,any of same that is inconsistent with or in conflict with the presentdocument, or any of same that may have a limiting affect as to thebroadest scope of the claims now or later associated with the presentdocument. By way of example, should there be any inconsistency orconflict between the description, definition, and/or the use of a termassociated with any of the incorporated material and that associatedwith the present document, the description, definition, and/or the useof the term in the present document shall prevail.

In closing, it is to be understood that the embodiments of theapplication disclosed herein are illustrative of the principles of theembodiments of the application. Other modifications that may be employedmay be within the scope of the application. Thus, by way of example, butnot of limitation, alternative configurations of the embodiments of theapplication may be utilized in accordance with the teachings herein.Accordingly, embodiments of the present application are not limited tothat precisely as shown and described.

1-6. (canceled)
 7. A method for segmenting a medical image, including:acquiring a first type of medical image via reconstructing a first typeof data obtained in a scan of an object using a first scanner, whereinthe first type of medical image comprises a plurality of voxels;acquiring a second type of data for reconstructing a second type ofmedical image, wherein the second type of data is obtained in a scan ofthe object using a second scanner; obtaining at least an unambiguousregion and at least an air/bone ambiguous region by executingpreliminary segmentation to the first type of medical image, wherein theair/bone ambiguous region includes air voxels and bone voxelsundistinguished from each other; generating an initial attenuation mapby assigning attenuation coefficients to voxels belonging to differentregions of the segmented first type of medical image, wherein the voxelsof the at least one air/bone ambiguous region are assigned at least onespecific attenuation coefficient without differentiating the air voxelsand the bone voxels in the at least one air/bone ambiguous region;generating the second type of medical image and an estimated attenuationmap by performing, based on the second type of data and the initialattenuation map, iterative reconstruction; reassigning attenuationcoefficients to the voxels of the first type of medical image based onthe estimated attenuation map; and segmenting the first type of medicalimage into a plurality of sub-regions distinguished from each otherbased on the reassigned attenuation coefficients.
 8. The method of claim7, wherein the first type of medical image is a magnetic resonance (MR)image, the second type of medical image is a positron emissiontomography (PET) image.
 9. The method of claim 7, wherein theunambiguous region comprises at least a soft-tissue region.
 10. Themethod of claim 9, wherein values of the voxels in the soft-tissueregion are similar to each other, and values of the voxels in theair/bone ambiguous region are similar to each other.
 11. (canceled) 12.The method of claim 7, wherein the at least one specific attenuationcoefficient is equal to an attenuation coefficient of water. 13.(canceled)
 14. The method of claim 9, wherein the unambiguous regioncomprises a first soft-tissue region and a second soft-tissue region,and the voxels of the first soft-tissue region are assigned a firstattenuation coefficient, and the voxels of the second soft-tissue regionare assigned a second attenuation coefficient.
 15. The method of claim14, wherein the first attenuation coefficient or the second attenuationcoefficient is updated after each iteration.
 16. The method of claim 7,wherein the attenuation coefficient of each voxel in the air/boneambiguous region is updated after each iteration.
 17. A system forsegmenting a medical image, the system comprising: at least one imageprocessor configured to: acquire a first type of medical image viareconstructing a first type of data obtained in a scan of an objectusing a first scanner, wherein the first type of medical image includesa plurality of voxels; acquire a second type of data for reconstructinga second type of medical image, wherein the second type of data isobtained in a scan of the object using a second scanner; obtain at leasta soft-tissue region and at least an air/bone ambiguous region byexecuting preliminary segmentation to the first type of medical image,wherein the air/bone ambiguous region includes air voxels and bonevoxels undistinguished from each other, and the soft-tissue regionincludes only voxels of soft-tissue; generate an initial attenuation mapby assigning attenuation coefficients to voxels belonging to differentregions of the segmented first type of medical image, wherein the voxelsof the at least one air/bone ambiguous region are assigned at least onespecific attenuation coefficient without differentiating the air voxelsand the bone voxels in the at least one air/bone ambiguous region;generate the second type of medical image and an estimated attenuationmap by performing, based on the second type of data and the initialattenuation map, iterative reconstruction; and reassigning attenuationcoefficients to the voxels of the first type of medical image based onthe estimated attenuation map.
 18. The system of claim 17, wherein theat least one image processor is further configured to segment theair/bone ambiguous region into a first region, a second region, and athird region, wherein the first region comprises only voxels of a rib,the second region comprises only voxels of a spine, and the third regioncomprises only voxels of a lung.
 19. The system of claim 18, wherein theattenuation coefficient corresponding to the bone voxels in the firstregion are different from the attenuation coefficient corresponding tothe air voxels in the second region.
 20. The system of claim 17, furthercomprising: the first scanner configured to obtain the first type ofdata by performing a first scan of an object; the second scannerconfigured to obtain the second type of data by performing a second scanof the object; and a display configured to simultaneously display thefirst type of medical image and the second type of medical image in anoverlaying manner.
 21. The system of claim 17, wherein the first type ofmedical image is a magnetic resonance (MR) image, and the second type ofmedical image is a positron emission tomography (PET) image.
 22. Thesystem of claim 17, wherein the first scanner is an MR scanner, and thesecond scanner is a PET scanner.
 23. The system of claim 17, wherein thegenerating of the second type of medical image and the estimatedattenuation map by performing, based on the second type of data and theinitial attenuation map, iterative reconstruction includes: generatingan initial second type of medical image based on the second type of dataand the initial attenuation map; performing a plurality of iterationsfor generating the second type of medical image and the estimatedattenuation map, each iteration of the plurality of iterationsincluding: generating an updated attenuation map based on a priorattenuation map and a prior second type of medical image, the priorsecond type of medical image and the prior attenuation map being theinitial second type of medical image and the initial attenuation map ina first iteration of the plurality of iterations, or an updated secondtype of medical image and an updated attenuation map generated in aprior iteration, respectively; and generating an updated second type ofmedical image based on the second type of data and the updatedattenuation map; and designating the updated second type of medicalimage and the updated attenuation map generated in a last iteration ofthe plurality of iterations as the second type of medical image and theestimated attenuation map.
 24. The method of claim 7, wherein the firstscanner is an MR scanner, and the second scanner is a PET scanner. 25.The method of claim 7, wherein the segmenting of the first type ofmedical image into the plurality of sub-regions distinguished from eachother based on the reassigned attenuation coefficients includes:segmenting the air/bone ambiguous region into a first region and asecond region, wherein the first region includes only the bone voxelsand the second region includes only the air voxels.
 26. The method ofclaim 7, wherein the generating of the second type of medical image andthe estimated attenuation map by performing, based on the second type ofdata and the initial attenuation map, iterative reconstruction includes:generating an initial second type of medical image based on the secondtype of data and the initial attenuation map; performing a plurality ofiterations for generating the second type of medical image and theestimated attenuation map, each iteration of the plurality of iterationsincluding: generating an updated attenuation map based on a priorattenuation map and a prior second type of medical image, the priorsecond type of medical image and the prior attenuation map being theinitial second type of medical image and the initial attenuation map ina first iteration of the plurality of iterations, or an updated secondtype of medical image and an updated attenuation map generated in aprior iteration, respectively; and generating an updated second type ofmedical image based on the second type of data and the updatedattenuation map; and designating the updated second type of medicalimage and the updated attenuation map generated in a last iteration ofthe plurality of iterations as the second type of medical image and theestimated attenuation map.
 27. The method of claim 8, wherein the MRimage is segmented based on at least one approach selected from athresholding approach, a region growing approach, a histogram approach,an atlas-guided approach, or a clustering approach.
 28. The method ofclaim 9, wherein the obtaining the air/bone ambiguous region includes:excluding the voxels of the at least a soft-tissue region from the firsttype of medical image.